Method for inspecting the surface of a moving strip by prior classification of the detected surface irregularity

ABSTRACT

A method for inspecting a surface of a moving strip to detect surface defects, which includes forming at least one digital picture of at least one face of the strip, in which the digital picture is made up of a set of successive rows of picture elements, each of which is assigned a digital value. The method also includes filtering the at least one digital picture to detect surface irregularities by detecting relative variations in the digital values, and processing the at least one filtered digital picture to identity a type of surface defect corresponding to each detected irregularity. Further, prior to the processing step, an overall characterization of each of the irregularities is performed by determining, a value of predetermined parameters characteristic of surface defects, and a prior classification of the irregularities is performed based on the determined values of the parameters, according to a set of predefined classes. The processing step is performed on each class, and the predetermined parameters characteristic of the surface irregularities is less than a total number of parameters used to identify the defect.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method for inspecting the surface ofa moving strip, in particular of rolled sheet metal moving at highspeed, for detecting surface defects, as well as to a system employingsuch an inspection method.

2. Discussion of the Background

In conventional types of surface inspection systems, in which thesurfaces of a moving strip are examined automatically, a surface isinspected by forming at least one digital picture of at least one of thefaces of the strip, these being made up of a set of successive rows ofpicture elements which are each assigned a digital value. The at leastone digital picture is filtered in order to detect surfaceirregularities by detecting relative variations in the digital values,and the surface irregularities are processed in order to identify thetype of surface defect corresponding to each detected irregularity.

According to this inspection technique, the surface irregularities aregenerally processed by identifying the defect, from a set of defectsliable to occur on the surface being inspected, which corresponds toeach irregularity. The analysis of the irregularities is thus carriedout in the same way, irrespective of the nature of the detectedirregularities.

This type of surface inspection system consequently has relatively lowprocessing speeds, in particular because of the nature of the processingstep, which requires a large number of relatively long and complexcalculation operations.

SUMMARY OF THE INVENTION

The object of the invention is to overcome these drawbacks and toprovide a surface inspection method which allows prior sorting of thedetected surface irregularities to be carried out, irrespective of thenature of the surface being inspected.

It therefore relates to a method for inspecting the surface of a movingstrip, of the aforementioned type, comprising the steps consisting in:

using photographic means to form at least one digital picture of atleast one of the faces of the strip, the digital picture being made upof a set of successive rows of picture elements, each of which isassigned a digital value;

filtering the said at least one digital picture in order to detectsurface irregularities by detecting relative variations in the saiddigital values; and

processing the said at least one filtered digital picture in order toidentify the type of surface defect corresponding to each detectedirregularity;

characterized in that, prior to the step of processing the said at leastone digital picture, an overall characterization of the irregularitiesis carried out by determining, for each of them, the value ofpredetermined parameters characteristic of surface defects, and a priorclassification of the said irregularities is carried out on the basis ofthe determined values of the said parameters, according to a set ofpredefined classes, the said processing step being carried out on eachclass.

Since the detected irregularities are classed beforehand according to aset of classes, on each of which the image processing is carried out, itcan be seen that the latter is speeded up considerably with the aid ofthis prior trimming step.

Further, this prior classification makes it possible to reduce theincidence of recognition errors and therefore improve the quality of theidentification.

The method according to the invention may furthermore have one or moreof the following characteristics:

each predetermined parameter representing a general reference axis in aspace whose dimensions correspond to the said parameters, regions whicheach correspond to one of the said predefined classes are delimited inthe said space, prior to the said prior classification, eachirregularity is represented in the said space by a point whosecoordinates are the values of the said parameters, and the said priorclassification is carried out by identifying the region to which eachpoint belongs, and by assigning the corresponding irregularity to theclass corresponding to the said region;

a second way of characterizing the irregularities is determined for eachpredefined class whose number of characteristic parameters is less thanthe number of characteristic parameters for overall characterization,and subsequent to the prior classification step, the value of thecharacteristic parameters of the second characterization method specificto the said class to which the irregularity belongs is determined foreach detected irregularity, on the basis of the values of thecharacteristic parameters for overall characterization;

a simplified reference frame for representing the irregularities isdetermined for each region, the number of axes of which is less than thenumber of general reference axes, and subsequent to the priorclassification step, a step of changing reference frame from the saidgeneral reference to the said simplified reference frame specific to theregion to which the irregularity belongs is carried out for eachirregularity represented;

the step of processing the irregularities includes a first step ofidentifying the defect corresponding to each irregularity, from a set oftypes of defects specific to the class to which the said irregularitybelongs, and a second step of classifying the said identified defectwith a view to confirming and refining the classification resulting fromthe said first classification step;

the method includes a step of qualifying the types of defects identifiedin terms of a first type of defects identified certainly and/orprecisely and a second type of defects identified uncertainly and/orimprecisely, and in that the said second classification step is carriedout only on the defects of type qualified as uncertain and/or imprecise;

the method further includes a step of grouping together identifieddefects using a set of predefined criteria, in particular geometricand/or topographical criteria;

the method furthermore includes the steps of counting the number ofidentified defects of the same type per unit length, and of comparingthe said number of defects of each type with a predetermined thresholdvalue representative of the minimum number of defects on the basis ofwhich the said defects are liable to exhibit a periodic character, witha view to detecting periodic defects;

subsequent to the step of determining the value of the said parameters,and before the said prior classification step, a specific classificationof the irregularities is carried out according to a set of elementaryclasses, and the population of the said elementary classes is analysedwith a view to detecting periodic defects;

subsequent to the filtering step, in response to detection of a pictureelement of an irregularity, a storage zone for picture element rowswhich are successively delivered by the photographic means and includeat least one picture element corresponding to at least one irregularityis defined in a memory, each storage zone is segmented into suspectzones each having at least one surface irregularity, suspect zones whichare contained in successive storage zones and correspond to the sameirregularity are paired, and the total number of rows of pictureelements of the paired suspect zones is compared with a threshold forlarge-length defect detection, and if the said threshold is exceeded,the said step of processing the said at least one filtered digitalpicture is carried out only on one of the said paired suspect zones, theresult of the processing being assigned to the other paired suspectzones.

The invention also relates to a system for inspecting the surface of amoving strip for implementing a method as defined above, characterizedin that it has means for photographing at least one of the faces of thestrip, a memory for storing at least one picture of the strip in theform of rows and columns of picture elements which are each associatedwith a digital value, a circuit for filtering the said at least onedigital picture in order to detect surface irregularities on the strip,by detecting relative variations in the said digital values, and a unitfor processing signals which is connected to the said filtering circuitand comprises means for calculating values of characteristic parametersof surface defects, means for classing the detected irregularitiesaccording to a set of clauses predefined on the basis of the values ofthe said parameters and means for identifying each irregularity from aset of types of defects liable to correspond to the said irregularity.

BRIEF DESCRIPTION OF THE DRAWINGS

Other characteristics and advantages will become apparent from thefollowing description, which is given solely by way of example and withreference to the appended drawings, in which:

FIG. 1 is an overall diagram of one embodiment of a surface inspectionsystem according to the invention;

FIG. 2 represents a part of a picture which is delivered by thephotographic means of the system in FIG. 1 and is stored in the memory;

FIGS. 3a to 3 e represent various pictures of the surface of a stripduring a step of dividing pictures;

FIG. 4 is a flow chart illustrating the overall way in which the systemin FIG. 1 operates;

FIG. 5 is a flow chart showing the various steps of processing thefiltered digital images;

FIGS. 6a and 6 b are diagrams which, as a function of the length and thewidth of the surface defects, show the various classes of defects,respectively for a semi-finished product (DKP) and for a galvanizedproduct; and

FIG. 7 is a flow chart showing the steps in a programme for analyzingdetected surface defects.

DESCRIPTION OF THE PREFERRED EMBODIMENTS.

The system represented in FIG. 1 is intended for detecting surfacedefects of a strip 10 which is moving at high speed, for example rolledsheet metal leaving a rolling line.

The surfaces of the sheet metal 10 are inspected using a photographiccamera 12 which delivers digital pictures of the surface of the strip toa filtering stage 14.

In the illustrative embodiment represented, the system has a singlephotographic camera 12 aimed at one of the surfaces of the strip, butthe system may of course be equipped with two cameras designed to formimages of each surface of the strip 10.

The photographic camera 12 may consist of any type of camera suitablefor the use in question, whose field width is substantially equal to thewidth of the inspection zone of the strip 10, which inspection zone mayconsist of the entire width of the strip. The camera may thus be made upof one or more matrix cameras delivering pictures with finite length, asregards the direction in which the strip is moving, or of one or morelinear cameras delivering pictures of infinite length.

If one matrix or linear camera is not sufficient to cover the entirewidth of the inspection zone of the strip, a plurality of camerasdistributed over the width of the strip will be used.

Referring to FIG. 2, the photographic camera 12 forms rows i of Mpicture elements I_(i,j) or pixels, which can be addressed foridentifying the pixels over the length of the strip 10, by the line No.i and, over the width, by column No. j of picture elements, each pictureelement being associated with a digital value representing a grey level.

The rows of picture elements are stored in a memory 18 of the filteringstage under the direction of a management circuit 20.

According to a first example, the photographic camera consists of alinear camera supplying the memory 18 with 10,000 rows of 2048 pixelsper second, these rows being stored in the memory at successiveaddresses.

According to another example, the photographic camera consists of twomatrix cameras which are distributed over the width of the strip inorder to cover the width of the strip and are designed to take 10pictures/s. Each picture delivered by one single camera consists of 1024rows of 1024 pixels, which are delivered by the memory 18.

The photographic system thus continuously outputs rows of pictureelements, each picture element being associated with a digital valuerepresenting a grey level. It will be understood that it is synchronizedby lines if a linear camera is involved, and synchronized by line groupsif a matrix camera is involved.

Referring again to FIG. 1, it can be seen that the filtering stage 14furthermore has a filtering circuit 21 consisting of an image processingoperator which detects relative variations in the digital values of thepicture elements, or pixels, in order to detect surface irregularities.

The filtering circuit preferably consists of a contour detectioncircuit, for example a detector of the “Prewitt” type, which detectsvariations in grey levels between picture elements lying close to oneanother, which makes it possible to detect zones of the sheet metal 10having surface irregularities.

As shown by FIG. 1, the output of the filtering circuit 14 is connectedto a signal processing unit 22 which includes a first stage 24 forsegmenting the digital pictures into picture element zones which eachdefine a surface irregularity detected by the filtering stage 14, and asecond signal processing stage 26, consisting of a calculating circuit28 associated with a corresponding memory 30 which holds processingalgorithms for recognizing and identifying surface defects, for eachzone having a surface irregularity.

The system represented in FIG. 1 is furthermore provided with a displaydevice 32 connected to the output of the processing unit 22, one inputof which is connected to an output of the calculating circuit 28 andmakes it possible to display the detected surface defects associatedwith information relating to the type of defect and parametersrepresenting the severity of these defects, as will be described indetail below.

The way in which the system which has just been described operates willnow be described with reference to FIGS. 2 to 7.

FIG. 3a represents a part of the sheet metal 10 which has a set ofsurface irregularities such as 34.

The field of the photographic camera 12 preferably covers the entirewidth of the strip 10. Referring to FIG. 4, during a first step 36, thephotographic camera takes successive rows of picture elements of thesurface of the strip 10, these picture elements being stored, in thememory 18, associated with a grey level value.

During this first photographing step 36, if necessary the managementcircuit 20 merges the pictures delivered by the photographic camera 12,by grouping together the successive pixels, on the one hand in thedirection of the width of the strip 10 in the case when a plurality ofcameras are used to cover the full width of the inspection zone, inorder to obtain in the memory 18 a picture whose width corresponds tothat of the inspected zone, and, on the other hand, in the direction ofthe length of the strip 10, in the case when the photographic camera 12uses one or more matrix cameras, by merging the groups of rows of pixelsdelivered in succession.

The picture stored in the memory 18, referred to below as the “rawpicture”, is made up of a set of picture elements I_(i,j,) i denotingthe address of the row in the memory, ranging from 1 to N, and jdenoting the number of a picture element of each row and varying from 1to M, M being equal for example to 2048, each picture element beingassociated with a grey level digital value.

It should be noted that the value N depends on the capacity of thememory. This capacity should be arranged so as to store a sufficientnumber of rows, in terms of the subsequent processing to be carried out.For example, in order to store a picture corresponding to a 15 m lengthof sheet metal with a number of rows of picture elements equal to1024/m, N is preferably equal to 15,360 rows.

When the capacity of the memory is saturated, rows which arrivesuccessively are stored instead of the oldest rows of pixels storedbeforehand and normally processed.

When the memory 18 is saturated and the oldest rows of pixels have notbeen processed, a saturation alarm is emitted in order to indicate thata zone of the strip will not be inspected.

In this case, the uninspected zone is located on the strip byidentifying and storing in a file the successive rows which have notbeen stored, for example with a view to a statistical analysis ofuninspected portions of the strip.

However, in view of the average speed with which the strip moves and theaverage density of surface irregularities to be identified for a giventype of strip, it is possible to determine an average required computingpower corresponding to an average processing speed above which there isno longer any risk in practice of erasing unprocessed rows.

The processing modules will therefore preferably be dimensioned so thatthe instantaneous processing speed is higher than this average speed.

Thus, further to its function of merging the pictures, the memory 18fulfils a buffer function making it possible to accommodate variations,and in particular increases, in the processing workload owing to anincrease in the density of surface irregularities.

During the following step 38, a binary picture representing contourlines of surface irregularities is associated with each picture storedin the memory 18.

To do this, during this step, the successive rows of raw pictureelements are filtered by the filtering circuit 21, which as mentionedabove consists, for example, of a conventional type of two-way Prewittfilter, having the function of detecting the variations in grey levelsof the raw picture elements which signify the existence of surfaceirregularities, with a view to determining their contour which iswritten in the associated binary picture.

In the embodiment described, it is assumed that the filter used is aPrewitt filter, but any other type of filter suitable for the use inquestion may of course also be used.

The Prewitt filter identifies the position of the surface irregularitycontour by detecting, on each row of a raw image, picture elementsliable to belong to an irregularity contour line, these picture elementsbeing referred to below as “suspect pixels”.

The filter which is used here assigns a digital value “1” to each binarypicture element associated with each suspect pixel of the raw picturedelivered by the photographic camera 12, the other pixels of the binarypicture being kept at 0.

This filtering step 38 thus makes it possible to form a binary picturein the memory 18, made up of a set of binary picture elements B_(i,j,)to each of which a binary value equal to 1 is assigned for a pixelbelonging to a contour of an irregularity, and equal to a zero value fora pixel not belonging to a contour of a surface irregularity.

During the next step 40, the binary picture stored in the memory 18 isprocessed using a conventional connectivity operator which applies amask to this picture in order to force to the digital value “1” pixelsof the binary picture which have a zero value and lie between tworelatively close suspect picture elements, with a view to obtaining anddefining continuous lines for each contour detected.

After having undergone this processing, the raw and binary pictures arecleaned to eliminate the spots delimited by a contour whose area is lessthan a specific threshold, for example 3×3 pixels. This provides abinary picture which is superimposed on the raw picture delivered by thephotographic camera 12 and shows the contours defining the surfaceirregularities detected in the raw picture. The binary picture and theraw picture are then ready for processing.

During the next step 42, the management circuit 20 successively analyzeseach row of the stored binary picture in order to detect the binaryelements with value “1”, that is to say those which are suspect. Once asuspect pixel is detected, the management circuit 20 identifies thenumber of the corresponding row, opens a storage zone of predeterminedcapacity in the form of a window in the memory 18 (step 44) startingfrom this row number, and keeps this window open so long as themanagement circuit detects suspect pixels in the following rows.

This window, referred to below as the “suspect window”, thus containssuspect pixels, that is to say ones liable to belong to a surfaceirregularity.

The management circuit 20 closes the suspect window again when nosuspect pixel has any longer been detected in a predetermined number ofsuccessive rows of the binary picture, recording the number of the lastrow in which a suspect pixel has been identified.

The suspect window thus defined in the memory 18 represents a rawpicture segment associated with a corresponding binary picture, andcontains at least one surface irregularity which it is desired toidentify and recognize.

In particular, the window open during step 44 is kept open so long asthe number of the last successive rows of picture elements stored in thewindow and containing no suspect pixel does not exceed a predeterminedthreshold number of successive binary rows, this threshold being atleast equal to 1.

During the next step 45, the number of successive rows of pictureelements not containing any suspect pixel is thus compared with thisthreshold number and, if they are equal, the suspect window is closed(step 46).

Furthermore, during step 47, the number of rows recorded in the openwindow is compared with a predetermined threshold termed “long-lengthwindow detection” or “detection of a long defect”.

This predetermined threshold corresponds to the predetermined maximumcapacity of the storage zones in the memory 18.

If the number of rows recorded is higher than this threshold, the windowis closed (step 48) and the decision is taken, during the next step 50,that the window is a window termed “long-length suspect”, which containsa surface irregularity for which the number of rows of picture elementsis higher than the long defect detection threshold.

It will also be noted that, in the embodiment example described, thesuspect windows are opened successively.

The surface inspection method continues with phases of dividing thesuspect windows stored in the memory 18 into zones which are referred toas “suspect zones” and each have a surface irregularity, using eitherthe component corresponding to the raw picture, or the componentcorresponding to the binary picture.

To do this, during the next steps 58 to 64, the stage 24 makes acalculation, using suitable means, for example software means, of theaccumulation profile of the digital values or the binary values,respectively for each raw picture or each binary picture, on the onehand in the longitudinal direction and, on the other hand, in the widthdirection, by projecting the digital values or binary values along twoperpendicular axes and by thresholding the profiles so as to definesuspect regions which each incorporate a surface irregularity.

Although the calculation of this profile can be carried out on the basisof the digital values associated with the picture elements of the rawpicture or on the basis of the binary values of the picture stored afterprocessing, in the rest of the description it will be assumed that theprocessing of the picture is carried out on the basis of the binarypicture.

This calculating operation starts with a phase of segmenting eachsuspect window into a suspect strip encompassing irregularities, eachstrip then being segmented into one or more suspect zones.

Firstly, during step 58, the stage 24 uses a calculating circuit 24-a(FIG. 1) to calculate the sum of the binary values of each row of thesuspect window in order to obtain, over M columns, a first transverseprofile in the direction of the width of the strip. The curverepresented in FIG. 3b is thus obtained.

During the next step 60, this profile is presented to the input of aframing circuit 24-b, in order to be framed so as not to separatepicture elements of an irregularity which lie close to one another.

The framing circuit 24-b may consist of any type of suitable filter,such as a finite-impulse response filter FIR, or infinite impulseresponse filter IIR, but preferably consists of a filter of themoving-window type, making it possible to deliver a framed profile r(x)whose values are determined according to the following equation:$\begin{matrix}{{r(x)} = {\sum\limits_{i = {- K}}^{K}{{F\left( {x - i} \right)} \times {Q(i)}}}} & (1)\end{matrix}$

in which K denotes the width of the moving window,

F(x−i) denotes the value of the column (x−i) of the profile to beframed,

Q denotes the coefficient of the moving-window filter, for examplechosen to be equal to 1, and

x denotes the column number of the framed profile.

The profile framed in this way is then thresholded using a thresholdingcircuit 24-c, during the next step 62, by comparison with anirregularity-detection threshold value.

The framed and thresholded profile represented in FIG. 3c is thusobtained, defining suspect strips which are represented using dashes inFIG. 3a and each encompass one or more surface irregularities.

As mentioned above, the suspect strips defined in this way are thensegmented into suspect strips which each have a surface irregularity.

To do this, during the next step 64, steps 58, 60 and 62 are carried outagain and applied independently to each row of picture elements of eachsuspect strip, so as to obtain an accumulation profile of the binaryvalues in the longitudinal direction, as represented in FIG. 3d.

This longitudinal profile is then framed and thresholded, as before, inorder to obtain the picture represented in FIG. 3e, in which suspectzones such as 66 are defined, which each delimit a detected surfaceirregularity, it being of course possible for each irregularity toinclude a plurality of irregularity segments or objects.

Each suspect zone thus defined therefore contains a raw picture segmentand the corresponding binary picture segment.

The suspect zones 66 thus delimited are preferably furthermore presentedto the input of a second calculating circuit 24-d, connected to theoutput of a thresholding circuit 24-c, by means of which theirregularities with small sizes are eliminated.

To do this, during the next step 68, each suspect zone of the binarypicture is processed independently using a conventional labellingalgorithm with a view to delimiting objects constituting a surfaceirregularity, each object being defined by a set of suspect pictureelements in contact with one another.

The area of each object is then calculated, as is the average area ofthe objects belonging to a given suspect zone.

The objects with small sizes are eliminated from the processing. To dothis, a decision is made to eliminate those objects whose individualarea is less than a predetermined percentage of the calculated averagearea.

This provides, at the output of the calculating circuit 24-d, suspectzones which each contain an irregularity and from which the smallobjects have been eliminated.

These suspect zones cleaned in this way are then stored in the memory 30of the calculating circuit 28 with a view to being processed, as will bedescribed in detail below with reference to FIG. 5.

It should be noted that the calculating 24-a, framing 24-b, andthresholding 24-c and calculating 24-d circuits are circuits of aconventional type. They will not therefore be described in detail below.

In the case when a suspect window has been qualified as a suspect windowof large length during the previous step 50, the step of processing thepictures is preceded by a phase of eliminating certain suspect zonesfrom the processing, which makes it possible to reduce the workload ofthe calculating circuit 28.

To do this, as soon as a suspect window of large length is detected(steps 47, 48 and 50) and cut into suspect zones as described above,during the next step 70 at least one suspect zone of this window whoselower row of picture elements belongs to that of the window isidentified. This suspect zone identified in this way is then qualifiedas a “bottom-cut suspect zone”.

The suspect window following a suspect window of large length isqualified as an “extension suspect window”.

It will be understood that an extension suspect window may also be oflarge length.

After cutting, as described above, an extension suspect window insuspect zones, the at least one suspect zone of this window whose upperrow of picture elements belongs to that of the window is identified,this suspect zone then being qualified as a “top-cut suspect zone” or“extension suspect zone” (step 71).

The “bottom-cut” suspect zones of the window of large length and the“top-cut” ones of the extension suspect window (step 72) are thenpaired.

During the next step 73, whether the extension suspect window is itselfof large length is determined. If so, at least one suspect zone of thiswindow whose lower row of picture elements belongs to that of the windowis identified, this suspect zone then being qualified as before as a“bottom-cut suspect zone”, and the same processing is carried out forrecomposing this suspect zone with the “top-cut” suspect zones of thenext window, termed extension (step 74).

As the pairing or association of the cut suspect zones from one windowto the next continues, the length of each defect is updated. During thenext step 75, the processing unit 22 compares the length of each defectwith the length of a window of large length, that is to say with thelong defect detection threshold mentioned above.

As soon as this length exceeds that of a window of large length, thedefect is qualified as being a long defect (step 76) and a “long defectgroup” is opened, which is defined by a zone of the memory of theprocessing stage in which all the successive cut and associated suspectzones which actually constitute the same defect, referred to as a “longdefect”, are placed.

All the extension suspect zones which belong to “long defect” groups arethen eliminated from the image processing; thus, in each “long defect”group, the image processing is carried out only on the first suspectzone (“bottom-cut”) and, to simplify the processing, the result of thisprocessing is assigned to all the extension suspect zones of the same“long defect” group.

As the matching or association of the cut suspect zones from one windowto the next continues, by updating the length of each defect associatedwith suspect zones which correspond to one another from one window tothe next, it can be observed during step 75 that this defect is not along defect.

The segmentation of such a defect cannot take place over more than twosuccessive windows, otherwise it would be qualified as a long defect.

In this case, a storage zone is opened in the memory 30 in the form of aso-called “recomposition” suspect zone, in which the two cut suspectzones of the same defect are placed, suitably joined and centered, thesize of the window being adapted in order to frame the said defect as inthe case of the uncut suspect zones (step 77).

The recomposition suspect zones are then processed like all the othersuspect zones.

Since the phase of segmenting the raw and binary pictures in suspectzones to be processed is now finished, the processing of each suspectzone defined during steps 58 to 68 is then carried out with theexception of the “long defect” group extension suspect zones.

The processing of each suspect zone will now be described with referenceto FIGS. 5 and 7.

This processing starts with a step 78 of calculating defectidentification parameters, generally qualified as a parameter extractionstep.

In a manner which is known per se, the nature is determined of theparameters which are liable to characterize the defects or surfaceirregularities of the strip to be inspected and are necessary forrecognizing them and identifying them accurately and reliably.

The method for calculating these parameters is also determined, inparticular on the basis of values of picture elements in the raw orbinary picture of a suspect zone containing the defect or the surfaceirregularity.

In a conventional way, these parameters generally include the length,width and area of a surface irregularity in a suspect zone, the averageintensity of the grey levels of the raw picture elements within thedefect, the standard deviation of these grey levels, etc.

The number of parameters needed for accurate and reliable recognition,referred to below as P, may be very high and as much as, for example,65.

With the nature and the method for calculation of the parameters of thedefects now being defined for a type of strip to be inspected,calculation of the P parameters for each suspect zone is then carriedout.

Each suspect zone or irregularity can thus be represented by a point ina P-dimensional space.

This high number P of parameters is a handicap, in view of the time andprocessing means for recognizing the suspect zones. In order to avoidthis handicap, or at least limit it, a trimming step 80 is carried outwhich makes it possible to simplify considerably the processing of eachsuspect zone by classing the irregularities according to a set oftrimming classes. This trimming step, which constitutes a priorclassification of the irregularities, according to a set of predefinedclasses, makes it possible to divide the overall problem of analysingthe irregularities into a set of problems which are simpler to process.

In particular, within each trimming class, a set of elementary classesor families of defects, the number of which is limited, is defined.

In order to make it possible to carry out the trimming step, it isnecessary to have provided a prior phase of defining the trimmingclasses and, where appropriate, their simplified associatedidentification, generally before the method according to the inventionis carried out.

This prior phase is specific to a type of strip to be inspected.

As an example of a prior phase leading to the definition of trimmingclasses, the following learning procedure is adopted.

A surface inspection, as described above as far as this stage of themethod, is carried out on a sufficient number of samples of the sametype of strip to lead to a sufficiently large and representativepopulation of suspect zones, in which each irregularity is representedby a point in the P-dimensional space mentioned above.

According to the generally known method of factorial correspondenceanalysis, the way in which these points are grouped together as cloudsin the space is identified.

It is then assumed that each region in the space delimiting a cloudmakes it possible to define a defect typology, and the defects in agiven cloud therefore have elements in common and will therefore be ableto be possibly represented in a simplified reference frame specific tothis cloud or to this typology.

In order to define the axes of a simplified reference frame specific toa typology or to a given cloud, the principal axes of inertia of thiscloud may be used, the positions and directions of which can becalculated in a manner which is known per se.

All the defects in a given class can thus be represented in the samesimplified reference frame in a space whose dimension is less than P,that is to say all the defects in a given class can be characterized bya reduced number of parameters, less than P.

By employing conventional mathematical methods, change-of-frame matricesare established which make it possible to convert from a representationof the defects in the P-dimensional space to a representation of thesame defect in a simplified reference frame with reduced dimensions.

In this prior phase intended to prepare the trimming, typologies or“trimming classes” of defects and a simplified reference frame fordefect representation, specific to each trimming class, have thus beendefined.

In a specific example, these trimming classes may be defined on thebasis of the length (L) or the width (w) of the irregularities;referring to FIGS. 6a and 6 b, 5 and 6 trimming classes are for exampledefined, respectively for “DKP” sheet metal and for galvanized sheetmetal, namely a small defect class (sm), a short thin defect class (st),a long thin defect class (lt), a short and medium defect class (sm), amedium and long defect class (ml) and a wide defect class (wi); asimplified representation reference frame being associated with eachclass.

After the parameter extraction step, the prior classification ortrimming step 80 proper can now be implemented.

To do this, each defect or surface irregularity of a suspect zone isdistributed into the various trimming classes defined above, on thebasis of the value of the P parameters of a defect and thecharacteristics which define these classes.

This prior distribution of the defects into trimming classes allows thedefect recognition to be simplified considerably by carrying out thisrecognition on each trimming class.

As a variant, all the defects of a given class are represented in thesimplified reference frame associated with this class, by using thechange-of-frame matrix for this class, applied to the P parameters. Thisthen leads to a simplified characterization of all the defects, using areduced number of parameters, which limits the amount of calculations tobe carried out during recognition.

The subsequent step 82 of the processing consists in recognizing andidentifying the defects of each trimming class.

The identification and recognition processing is specific to eachtrimming class and is generally defined beforehand on the basis of thetypes of defect which are liable to be encountered in each class.

This identification and recognition processing may consist in aclassification based, for example, on the method known as “Coulombspheres”.

Other known methods may also be used, such as the discriminant analysismethod, the decision tree method or the method which involvesdetermining the nearest neighbouring “K”.

According to the Coulomb spheres method, the defect typologies specificto a given trimming class are represented by spheres, identifiable byposition and size, in the simplified space associated with this class.

Each sphere corresponds to a type of defect and/or to a defectidentification name.

Thus, in order to recognize and identify a defect of a given trimmingclass, during step 83 the sphere to which the defect belongs isidentified and the identification name associated with this sphere isassigned (step 84).

This recognition and identification operation may advantageously becarried out very rapidly because, since the number of spheres and thenumber of parameters are reduced because of the previous trimming step,the classification calculations can be carried out on a reduced numberof criteria.

In the particular case when, within a given trimming class, a defect notbelonging to any sphere is encountered, it is assigned theidentification name of the closest sphere.

Thus, at the end of the step 84 of assignation of a defectidentification name to each irregularity, all the irregularities areidentified as corresponding to a particular type of defect.

The next step 86 consists in carrying out a second classification usinga second classification stage of the calculating circuit 28, on thebasis of a reduced number of classes in order, for example, to confirmthe result given by the first classification stage and to resolvecertain ambiguities which may have been found in the identification ofcertain defects, or in order, for example, to differentiate in terms ofmore accurate typology between defects of the same type which it wasdecided not to differentiate between in the first classification stage,for lack of sufficient classification performance at this level.

In order to make it possible to implement this second classificationstep 86, it is necessary to have provided a prior phase of qualifyingeach elementary class.

In this prior phase, statistical processing operations are carried outto validate or invalidate the classification carried out for identifyingthe defects, using the method which has just been described, so as toidentify the elementary classes which contain the most defectclassification errors.

These elementary classes, of which there is a reduced number, whichcontain the largest number of classing errors, are qualified as“elementary classes of uncertain identification”, and the others, whichcontain the fewest classing errors, are qualified as “elementary classesof certain identification”.

The second classification, implemented in step 86, is carried out onlyon the defects or irregularities classed in the elementary classes ofuncertain identification.

The second classification stage uses, for example, one of theclassification methods mentioned above.

It is suitable, for example, for validating or invalidating themembership, of defects, of these classes of uncertain identification. Inthe case of invalidation, the defect is then considered as not being adefect and is eliminated from the processing.

It may also be suitable, for example, for distributing the defects ofcertain elementary classes of uncertain identification into classes ofprecise identification, which are predefined using a more accuratetypology.

It should be noted that this additional classification is carried out ona very much reduced number of defect classes and can therefore beperformed very rapidly.

Following the steps 80 to 86, each defect is identified and recognized,that is to say assigned to an elementary class.

The image processing phase is concluded with a step 88 of merging thedata, during which certain defects are grouped together using criteriadefined beforehand, relating in particular to the geometry and thetopology of the defects (for example: distance of the defects from oneanother, identical position above and below the strip, proximity to theedge of the strip, etc.).

This merging phase makes it possible to remedy certain inaccuracieswhich may occur during the recognition of the defects and solve someparticular problems of confusion, without casting doubt on the resultswhich have already been confirmed.

The decision to group the defects together is made after addressinginformation arising from the close proximity of an object to berecognized, of the order of a metre for example, of other photographiccameras, (for example aimed at the other face of the strip), or datarelating to the processing of the strip (nature of the strip, end point,etc.).

In particular, it will be decided to group together defects for which anambiguity about their name remains, as well as defects of the samenature.

Furthermore, the defects which have particular proximity relationshipswill be grouped together, that is to say for example the defects locatedclose together, on the same face of the strip or on an opposite face, aswell as the defects lying in the same longitudinal or transversealignment.

For example, in the case of galvanized sheet metal, a defect of the“grain streak” type involves a multitude of surface irregularities lyingin the vicinity of the side edge of the sheet metal. In this case, theidentification of the defect is not fully reliable. This is because eachof these irregularities may be recognized as belonging to a “grainstreak”, or may be recognized individually as another type of defect, inparticular an “exfoliation” or a “blister”.

In this particular case, the irregularities which are located in thevicinity of the side edge of the sheet metal and are aligned with oneanother are merged, and they are identified as belonging to a defect ofthe “grain streak” type.

Similarly, according to another example, the defects located in the sameposition, on the upper and lower faces of the sheet metal, are groupedtogether during this merging step, and they are given the same name.

During this merging step, and as described above with reference to step76 in FIG. 4, the long defects cut when opening the suspect windows arealso grouped together while, as mentioned above, assigning the name ofthe defect of the suspect zone of large length to the defects of theextension suspect zones of the same group.

During this merging step, the population in each elementary defect classover a given length of strip is also analyzed, that is to say the numberof defects per unit length having the same identification.

This population is then compared with a predetermined threshold,referred to as the periodic defect presumption threshold. This thresholdis determined for the same given length of strip.

When the population of an elementary class exceeds the said threshold,it is assumed that defects of this class do indeed have a periodiccharacter.

In order to validate this character, a conventional method of detectingperiodic defects may be used.

For example, the histogram of the distance between each defect of thisclass is plotted and, if this histogram demonstrates periodicity(fundamental or harmonic), a specific “periodic defect” group is openedin the memory and the periodic defects of this class are groupedtogether in this same group.

According to a variant, this step of detecting and grouping together theperiodic defects may be carried out after the extraction of theparameters but before the identification and recognition, or even beforethe trimming or prior classification.

This variant therefore presupposes a specific classification processingwhich is relatively cursory since it needs to be based on characterizingthe defects according to a high number P of parameters and, in order todetect the periodic defects, the population of the elementary classesdefined in this specific classification is then analyzed.

This variant has the advantage of displaying a result which does notdepend on the performance of the recognition modules (trimming andupstream classification).

After having detected, recognized and possibly grouped together thedefects corresponding to detected irregularities, the subsequent phasein the inspection method consists in analyzing the defects with a viewto determining their severity, in order to make it possible to determinehow defective the strip is. This phase will now be described withreference to FIG. 7.

Beforehand, before the method is implemented, and for each class or typeof defect, on the basis of the various intrinsic repercussions possiblefor this type of defect, a set of subclasses is defined, each subclassbeing associated with a possible intrinsic repercussion of this type ofdefect. Each subclass may optionally be assigned an intrinsic severitycoefficient.

It can be seen that, at this stage, each surface irregularity isidentified and therefore characterized by characteristic parameters, inparticular by a reduced number of parameters.

During the first step 90 of this phase of analyzing the defects, thedefects, grouped together in a merged group in the previous step, areassimilated to a single defect referred to as a “merged defect”. To dothis, for these grouped defects, the parameters characterizing themerged defect are calculated by linear combination of the values of theparameters characterizing each defect or irregularity of the mergedgroup.

On the basis of the values of the parameters characterizing theungrouped defects and the merged defects, during the next step 92, anadditional classification of these defects is carried out according tothe set of subclasses specific to each type of defect.

This additional classification may be carried out using the same type ofmethods as those used during the defect recognition.

This additional classification leads to a result independent of thesubsequent uses of the sheet metal.

Following this additional classification, an “intrinsic defectivenessprofile” of the strip may be defined by a list giving the population ofeach “severity” subclass of each type or “elementary class” of defect,this population being normalized per unit length of strip; this profilemay for example be represented in the form of histograms of thepopulation of each subclass, which are arranged side by side in apredetermined order (subclass after subclass, class after class).

In parallel, for a given use of the strip, the same formalism (forexample: histograms in the same order) may be used to define a“permissible defectiveness profile”, namely, for each “severity”subclass of each possible type of defect, a maximum permissiblepopulation for this given use (still normalized per unit length ofstrip).

This “permissible defectiveness profile” is not defined “once and forall” for a given use; it may even vary according, for example, to thechange in the specifications of this use.

Next, in step 94, the intrinsic defectiveness profile of the inspectedstrip is compared with the permissible defectiveness profile for theintended use of the said strip.

Thus, during step 94, if it is found that the intrinsic defectivenessprofile of the inspected strip falls (or is contained) within thepermissible defectiveness profile for the proposed use of this strip,this strip is considered to be acceptable or validated for this use(step 96).

If this is not the case, this inspected strip is considered asunacceptable or “defective” with regard to this use (step 98).

To avoid having to discard this inspected strip, a search is then madefor the use within whose permissible defectiveness profile the intrinsicdefectiveness profile of this inspected strip falls (or is contained),and this strip is assigned to this other use.

This can be done because sheet metal having a predetermined number ofdefects of a given severity and a particular type may be not defectivefor one use, although it is defective for a different use.

For example, sheet metal having a scratch is defective if it is notrolled during a subsequent processing step, but it is considered as notbeing defective if it is re-rolled, since the scratches will then beflattened out.

The decisive advantage of this method for evaluating the defectivenessof a strip by measuring an intrinsic defectiveness profile is that thismeasurement is independent of the subsequent downstream use of thestrip, and of the variation pertaining to the criteria which need to besatisfied for this use.

The intrinsic defectiveness profiles of the inspected strips mayadvantageously be used, on the other hand, to monitor the variation andthe possible drifts in manufacturing processes for these strips, forexample between manufacturing runs; it is thus, for example, possible toidentify possible drifts in the behaviour of the rolling plant upstream.

The intrinsic defectiveness profiles of the inspected strips can also beused to identify drifts affecting the inspection system itself.

One possible simplified variant of the method for analysingdefectiveness is to assign to each “severity” subclass of types ofdefects a coefficient whose value depends on the severity estimated fora given use, and the defectiveness profile of a strip may be defined bythe sum of the populations of each subclass multiplied by thecorresponding coefficient. To validate this use, a check is then simplymade that the result obtained, namely the same sum, does not exceed apredetermined value defined for this use.

Other simplified variants based on the use of coefficients may also beenvisaged.

What is claimed is:
 1. A method for inspecting a surface of a movingstrip to detect surface defects, comprising: forming at least onedigital picture of at least one face of the strip, the digital picturebeing made up of a set of successive rows of picture elements, each ofwhich is assigned a digital value; filtering the at least one digitalpicture to detect surface irregularities by detecting relativevariations in the digital values; and processing the at least onefiltered digital picture to identify a type of surface defectcorresponding to each detected irregularity, wherein, prior to theprocessing step, an overall characterization of each of theirregularities is performed by determining, a value of predeterminedparameters characteristic of surface defects, and a prior classificationof the irregularities is performed based on the determined values of theparameters, according to a set of predefined classes, the processingstep being performed on each class, wherein the processing step isspecific for each class, wherein the predetermined parameterscharacteristic of the surface irregularities is less than a total numberof parameters used to identify the defect, wherein, each predeterminedparameter representing a general reference axis in a space whosedimensions correspond to the parameters, regions which each correspondto one of the predefined classes are delimited in the space, prior tothe prior classification, each irregularity is represented in the spaceby a point whose coordinates are the values of the parameters, and theprior classification is performed by identifying the region to whicheach point belongs, and by assigning the corresponding irregularity tothe class corresponding to the region, and wherein a simplifiedreference frame for representing the irregularities is determined foreach region, a number of axes of which is less then a number of generalreference axis, and subsequent to the prior classification step,changing a reference frame from the general reference to the simplifiedreference frame specific to the region to which the irregularity belongsfor each irregularity represented.
 2. The method according to claim 1,wherein the processing step includes identifying the defectcorresponding to each irregularity, from a set of types of defectsspecific to the class to which the irregularity belongs, and classifyingthe identified defect to confirm and refine the classification resultingfrom the classification step.
 3. The method according to claim 2,further comprising qualifying the types of defects identified in termsof a first type of defects identified certainly and a second type ofdefects identified uncertainly, wherein classifying the identifieddefect is performed only on the defects of type qualified as uncertain.4. The method according to claim 1, further comprising grouping togetheridentified defects using a set of predefined criteria.
 5. The methodaccording to claim 1, further comprising counting a number of identifieddefects of a same type per unit length, and comparing the number ofdefects of each type with a predetermined threshold value representativeof a minimum number of defects based on which of the defects exhibit aperiodic character to detect periodic defects.
 6. The method accordingto claim 1, wherein after the determining step, and before the priorclassification step, a specific classification of the irregularities isperformed according to a set of elementary classes, and a population ofelementary classes is analyzed to detect periodic defects.
 7. The methodaccording to claim 1, wherein after the filtering step, in response todetection of a picture element of an irregularity, a storage zone forpicture element rows successively delivered in the forming step andincluding at least one picture element corresponding to at least oneirregularity is defined in a memory, each storage zone is segmented intosuspect zones each having at least one surface irregularity, suspectzones which are contained in successive storage zones and correspond toa same irregularity are paired, and a total number of rows of pictureelements of the paired suspect zones is compared with a threshold forlarge-length defect detection, and if the threshold is exceeded, theprocessing step is performed only on one of the paired suspect zones, aresult of the processing being assigned to the other paired suspectzones.
 8. A system for inspecting a surface of a moving strip to detectsurface defects, comprising; a camera configured to form at least onedigital picture of at least one face of the strip, the digital picturebeing made up of a set of successive rows of picture elements, each ofwhich is assigned a digital value; a filtering circuit configured tofilter the at least one digital picture to detect surface irregularitiesby detecting relative variations in the digital values; and a signalprocessing unit configured to process the at least one filtered digitalpicture to identify a type of surface defect corresponding to eachdetected irregularity, wherein, prior to processing the at least onefiltered digital picture, an overall characterization of each of theirregularities is performed via the processing unit by determining, avalue of predetermined parameters characteristic of surface defects, anda prior classification of the irregularities is performed based on thedetermined values of the parameters, according to a set of predefinedclasses, the processing being performed on each class, wherein theprocessing of the signal processing unit is specific for each class,wherein the predetermined parameters characteristic of the surfaceirregularities is less than a total number of parameters used toidentify the defect, wherein, each predetermined parameter representinga general reference axis in a space whose dimensions correspond to theparameters, regions which each correspond to one of the predefinedclasses are delimited in the space, prior to the prior classification,each irregularity is represented in the space by a point whosecoordinates are the values of the parameters, and the priorclassification is performed by identifying the region to which eachpoint belongs, and by assigning the corresponding irregularity to theclass corresponding to the region, and wherein the processing unitdetermines a simplified reference frame for representing theirregularities for each region, a number of axes of which is less than anumber of general reference axis and subsequent to the priorclassification, changes a reference frame from the general reference tothe simplified reference frame specific to the region to which theirregularity belongs for each irregularity represented.
 9. The systemaccording to claim 8, wherein the processing unit identifies the defectcorresponding to each irregularity, from a set of types of defectsspecific to the class to which the irregularity belongs, and classifiesthe identified defect to confirm and refine the classification resultingfrom the classification.
 10. The system according to claim 9, whereinthe processing unit qualifies the types of defects identified in termsof a first type of defects identified certainly and a second type ofdefects identified uncertainly, wherein the processing unit classifiesthe identified defect only on the defects of type qualified asuncertain.
 11. The system according to claim 8, wherein the processingunit groups together identified defects using a set of predefinedcriteria.
 12. The system according to claim 8, wherein the processingunit counts a number of identified defects of a same type per unitlength, and compares the number of defects of each type with apredetermined threshold value representative of a minimum number ofdefects based on which of the defects exhibit a periodic character todetect periodic defects.
 13. The system according to claim 8, whereinafter the processing unit determines the value of predeterminedparameters, and before the prior classification of the irregularities,the processing unit performs a specific classification of theirregularities according to a set of elementary classes, and analyzes apopulation of elementary classes to detect periodic defects.
 14. Thesystem according to claim 8, wherein after the filtering circuit filtersthe at least one digital picture, in response to detection of a pictureelement of an irregularity, the processing unit defines is a memory, astorage zone for picture element rows successively delivered by thecamera forming the at least one digital picture and including at leastone picture element corresponding to at least one irregularity, eachstorage zone is segmented into suspect zones each having at least onesurface irregularity, suspect zones which are contained in successivestorage zones and correspond to a same irregularity are paired, and atotal number of rows of picture elements of the paired suspect zones iscompared with a threshold for large-length defect detection, and if thethreshold is exceeded, the processing unit processes only one of thepaired suspect zones, a result of the processing being assigned to theother paired suspect zones.